Analyst

Some careers have more impact than others. We are currently seeking an experienced professional to join our team in the role of Analyst, Financial Crime Detection and Digital Enablement Principal responsibilities Design and develop statistical, machine learning, or rule-based models for financial crime risk assessment, covering areas such as AML (Anti-Money Laundering), fraud, Antibribery and Corruption, Terrorist Financing, sanctions, etc. Identify risk factors and develop methodologies for quantifying financial crime risk across various business lines. Utilize appropriate modeling techniques, including logistic regression, decision trees, anomaly detection, and network analytics. Work with large datasets to extract insights, engineer features, and improve model performance. Ensure models comply with internal governance policies and external regulatory requirements Prepare and maintain model documentation, including methodology, assumptions, data sources, and validation results. Engage with model validation and internal audit teams to support independent model review and challenge processes. Requirements Experience in developing and managing statistical/ rule based / ML models. Proficiency in statistical programming languages such as Python. Strong understanding of model risk management frameworks and governance practices. Experience with data manipulation, feature engineering, and working with structured/unstructured data. Ability to communicate complex model concepts to non-technical stakeholders. Experience in banking, financial services, or regulatory environments. Hands-on experience with cloud-based data platforms like GCP Familiarity with machine learning frameworks and any visualization tool like Qlik sense.